{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import gym\n",
    "import itertools\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "\n",
    "\n",
    "from collections import defaultdict\n",
    "from envs.windy_gridworld import WindyGridworldEnv\n",
    "import plotting\n",
    "\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = WindyGridworldEnv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_epsilon_greedy_policy(Q, epsilon, nA):\n",
    "    \"\"\"\n",
    "    给定一个Q函数和epsilon，构建一个ε-贪婪的策略\n",
    "    \n",
    "    参数:\n",
    "        Q: 一个dictionary其key-value是state -> action-values.\n",
    "            key是状态s，value是一个长为nA(Action个数)的numpy数组，表示采取行为a的概率。\n",
    "        epsilon:  float \n",
    "        nA: action的个数\n",
    "    \n",
    "    返回值:\n",
    "        返回一个 函数，这个函数的输入是一个状态/观察(observation)，输出是一个长度为nA的numpy数组，表示采取不同Action的概率\n",
    "\n",
    "    \"\"\"\n",
    "    def policy_fn(observation):\n",
    "        A = np.ones(nA, dtype=float) * epsilon / nA\n",
    "        best_action = np.argmax(Q[observation])\n",
    "        A[best_action] += (1.0 - epsilon)\n",
    "        return A\n",
    "    return policy_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sarsa(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1):\n",
    "    \"\"\"\n",
    "    SARSA 算法: On-policy TD控制。找到最优的ε-greedy 策略。\n",
    "    \n",
    "    参数:\n",
    "        env: OpenAI 环境。\n",
    "        num_episodes: 采样的episodes次数。\n",
    "        discount_factor: 打折因子。\n",
    "        alpha: TD学习率(learning rate)。\n",
    "        epsilon:  ε-贪婪的ε，随机行为的概率\n",
    "    \n",
    "    返回值:\n",
    "         二元组(Q, stats).\n",
    "        Q 是最优行为价值函数，一个dictionary state -> action values.\n",
    "        stats是一个EpisodeStats对象，这个对象有两个numpy数值，分别是episode_lengths和episode_rewards，存储每个Episode的长度和奖励，用于绘图。\n",
    "    \"\"\"\n",
    "    \n",
    "    # 最终返回的Q(s,a)函数The final action-value function。\n",
    "    # 它是一个嵌套的dictionary state -> (action -> action-value)。\n",
    "    Q = defaultdict(lambda: np.zeros(env.action_space.n))\n",
    "    \n",
    "    # 参考返回值的注释。\n",
    "    stats = plotting.EpisodeStats(\n",
    "        episode_lengths=np.zeros(num_episodes),\n",
    "        episode_rewards=np.zeros(num_episodes))\n",
    "\n",
    "    # 通过Q(s,a)得到ε-贪婪的策略，注意Q变化后策略就随着改变了。\n",
    "    policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\n",
    "    \n",
    "    for i_episode in range(num_episodes):\n",
    "        if (i_episode + 1) % 100 == 0:\n",
    "            print(\"\\rEpisode {}/{}.\".format(i_episode + 1, num_episodes), end=\"\")\n",
    "            sys.stdout.flush()\n",
    "        \n",
    "        # 重置环境并且采样第一个Action\n",
    "        state = env.reset()\n",
    "        action_probs = policy(state)\n",
    "        action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\n",
    "        \n",
    "        for t in itertools.count():\n",
    "            # 采取行为A\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            \n",
    "            # 选择下一个行为A'\n",
    "            next_action_probs = policy(next_state)\n",
    "            next_action = np.random.choice(np.arange(len(next_action_probs)), p=next_action_probs)\n",
    "            \n",
    "            # 更新统计信息\n",
    "            stats.episode_rewards[i_episode] += reward\n",
    "            stats.episode_lengths[i_episode] = t\n",
    "            \n",
    "            # TD Update\n",
    "            td_target = reward + discount_factor * Q[next_state][next_action]\n",
    "            td_delta = td_target - Q[state][action]\n",
    "            Q[state][action] += alpha * td_delta\n",
    "    \n",
    "            if done:\n",
    "                break\n",
    "                \n",
    "            action = next_action\n",
    "            state = next_state        \n",
    "    \n",
    "    return Q, stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 200/200."
     ]
    }
   ],
   "source": [
    "Q, stats = sarsa(env, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best actions length 19, actions: [2, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 2, 2, 0, 2, 2, 2, 3, 3]\n"
     ]
    }
   ],
   "source": [
    "# 测试最终学到的策略\n",
    "state = env.reset()\n",
    "best_actions = []\n",
    "policy = make_epsilon_greedy_policy(Q, 0.1, env.action_space.n)\n",
    "for t in itertools.count():\n",
    "\n",
    "    # 必须ε-贪婪的策略，否则有可能它学到的第一步概率最大的就是碰墙，那么就会死循环\n",
    "    # 读者可以尝试一下去掉下面的注释，然后注释掉下面两行试试。\n",
    "    # 当然如果把上面的cell的200次迭代改成1000次，就会好很多。\n",
    "    # next_action = np.argmax(Q[state])\n",
    "\n",
    "    next_action_probs = policy(state)\n",
    "    next_action = np.random.choice(np.arange(len(next_action_probs)), p=next_action_probs)\n",
    "\n",
    "    best_actions.append(next_action)\n",
    "    next_state, reward, done, _ = env.step(next_action)\n",
    "\n",
    "\n",
    "    if done:\n",
    "        break\n",
    "\n",
    "    state = next_state\n",
    "\n",
    "print(\"best actions length %d, actions: %s\" %(len(best_actions), best_actions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 720x360 with 1 Axes>,\n",
       " <Figure size 720x360 with 1 Axes>,\n",
       " <Figure size 720x360 with 1 Axes>)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotting.plot_episode_stats(stats)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3.6-env",
   "language": "python",
   "name": "py3.6-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
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